Assessing inspiratory drive and effort in critically ill patients at the bedside
Bibliographic record
Abstract
Monitoring inspiratory drive and effort may aid proper selection and setting of respiratory support in patients with acute respiratory failure (ARF), whether they are intubated or not. Although diaphragmatic electrical activity (EAdi) and esophageal manometry can be considered the reference methods for assessing respiratory drive and inspiratory effort, respectively, various alternative techniques exist, each with distinct advantages and limitations. This narrative review provides a comprehensive overview of bedside methods to assess respiratory drive and effort, with a primary focus on patients with ARF. First, EAdi and esophageal manometry are described and discussed as reference techniques. Then, alternative methods are categorized along the neuromechanical pathway from inspiratory drive to muscular effort into three groups: (1) techniques assessing the respiratory drive: airway occlusion pressure (P0.1), mean inspiratory flow (Vt/Ti) and respiratory muscle surface electromyography (sEMG); (2) techniques assessing the respiratory muscle effort: whole-breath occlusion pressure (ΔPocc), pressure-muscle index (PMI), nasal pressure swing (ΔPnose), diaphragm ultrasonography (USdi), central venous pressure swing (ΔCVP), breathing effort (BREF) models, and flow index; (3) techniques and clinical parameters assessing the consequences of effort: tidal volume (Vt), electrical impedance tomography (EIT), dyspnea. For each, we summarize the physiological rationale, measurement methodology, interpretation of results, and key limitations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".